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Nowadays, there is tremendous growth in the Internet of Things (IoT) applications in our everyday lives. The proliferation of smart devices, sensors technology, and the Internet makes it possible to communicate between the digital and physical world seamlessly for distributed data collection, communication, and processing of several applications dynamically. However, it is a challenging task to monitor and track objects in real-time due to the distinct characteristics of the IoT system, e.g., scalability, mobility, and resource-limited nature of the devices. In this paper, we address the significant issue of IoT object tracking in real time. We propose a system called ‘TrackInk’ to demonstrate our idea. TrackInk will be capable of pointing toward and taking pictures of visible satellites in the night sky, including but not limited to the International Space Station (ISS) or the moon. Data will be collected from sensors to determine the system’s geographical location along with its 3D orientation, allowing for the system to be moved. Additionally, TrackInk will communicate with and send data to ThingSpeak for further cloud-based systems and data analysis. Our proposed system is lightweight, highly scalable, and performs efficiently in a resource-limited environment. We discuss a detailed system’s architecture and show the performance results using a real-world hardware-based experimental setup.

Delivery drones typically perform delivery by suspending the parcel vertically or landing the drone to drop off the package. However, because of the constrained landing area and the requirement for precise navigation, delivering items to customers who reside in multi-story apartment complexes poses a unique challenge. This research paper proposes a novel drone delivery system for multi-story apartment buildings with balconies that employ two methods for Vertical Grid Screening (VGS), i.e., Grid Screening (GS) and Square Screening (SS), to detect unique markers to identify the precise balcony that needs to receive the product. The developed drone has a frame size of 295 mm and is equipped with a stereo camera and a ranging sensor. The research paper also explores the scanning and trajectory methods required for autonomous flight to accurately approach the marker location. The proposed machine learning system is trained on a YOLOv5 model for image recognition of the marker, and four different models and batch sizes are compared. The 32-batch size with a 960 × 1280 resolution model provides an average of 0.97 confidence for an extended range. This system is tested outdoors and shows an accuracy of 95% for a planned trajectory with 398 ms detection time as a solution for last-mile delivery in urban areas.